
'''simple docstring'''



import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_lowercase          =						logging.get_logger("""transformers.models.encodec""")

_lowercase          =						{
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
_lowercase          =						{
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
_lowercase          =						{
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
_lowercase          =						{
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
_lowercase          =						{
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
_lowercase          =						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowercase          =						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowercase          =						[]
_lowercase          =						[]


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any					,      __lowerCamelCase :str					,      __lowerCamelCase :Optional[int]						):
						for attribute in key.split("""."""						):
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						)

						if weight_type is not None:
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						).shape
						else:
												_lowerCAmelCase    		=					hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
												    f' {value.shape} for {full_name}'						)

						if weight_type == "weight":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_g":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_v":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias":
												_lowerCAmelCase    		=					value
						elif weight_type == "running_mean":
												_lowerCAmelCase    		=					value
						elif weight_type == "running_var":
												_lowerCAmelCase    		=					value
						elif weight_type == "num_batches_tracked":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_ih_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_hh_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_ih_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_hh_l0":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_ih_l1":
												_lowerCAmelCase    		=					value
						elif weight_type == "weight_hh_l1":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_ih_l1":
												_lowerCAmelCase    		=					value
						elif weight_type == "bias_hh_l1":
												_lowerCAmelCase    		=					value
						else:
												_lowerCAmelCase    		=					value

						logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'						)


def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :str						):
						for key in ignore_keys:
												if key.endswith(""".*"""						):
																		if name.startswith(key[:-1]						):
																								return True
												elif ".*." in key:
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					key.split(""".*."""						)
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False


def A (__lowerCamelCase :Any					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict						):
						_lowerCAmelCase    		=					[]

						if model_name == "encodec_24khz" or "encodec_32khz":
												_lowerCAmelCase    		=					MAPPING_24K
						elif model_name == "encodec_48khz":
												_lowerCAmelCase    		=					MAPPING_48K
						else:
												raise ValueError(f'Unsupported model: {model_name}'						)

						for name, value in orig_dict.items():
												if should_ignore(__lowerCamelCase					,      __lowerCamelCase						):
																		logger.info(f'{name} was ignored'						)
																		continue

												_lowerCAmelCase    		=					False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					key.split(""".*."""						)
																								if prefix in name and suffix in name:
																														_lowerCAmelCase    		=					suffix

																		if key in name:
																								# HACK otherwise .embed gets initialized with .embed_avg too
																								if key.endswith("""embed"""						) and name.endswith("""embed_avg"""						):
																														continue

																								_lowerCAmelCase    		=					True
																								if "*" in mapped_key:
																														_lowerCAmelCase    		=					name.split(__lowerCamelCase						)[0].split("""."""						)[-2]
																														_lowerCAmelCase    		=					mapped_key.replace("""*"""					,      __lowerCamelCase						)
																								if "weight_g" in name:
																														_lowerCAmelCase    		=					"""weight_g"""
																								elif "weight_v" in name:
																														_lowerCAmelCase    		=					"""weight_v"""
																								elif "weight_ih_l0" in name:
																														_lowerCAmelCase    		=					"""weight_ih_l0"""
																								elif "weight_hh_l0" in name:
																														_lowerCAmelCase    		=					"""weight_hh_l0"""
																								elif "bias_ih_l0" in name:
																														_lowerCAmelCase    		=					"""bias_ih_l0"""
																								elif "bias_hh_l0" in name:
																														_lowerCAmelCase    		=					"""bias_hh_l0"""
																								elif "weight_ih_l1" in name:
																														_lowerCAmelCase    		=					"""weight_ih_l1"""
																								elif "weight_hh_l1" in name:
																														_lowerCAmelCase    		=					"""weight_hh_l1"""
																								elif "bias_ih_l1" in name:
																														_lowerCAmelCase    		=					"""bias_ih_l1"""
																								elif "bias_hh_l1" in name:
																														_lowerCAmelCase    		=					"""bias_hh_l1"""
																								elif "bias" in name:
																														_lowerCAmelCase    		=					"""bias"""
																								elif "weight" in name:
																														_lowerCAmelCase    		=					"""weight"""
																								elif "running_mean" in name:
																														_lowerCAmelCase    		=					"""running_mean"""
																								elif "running_var" in name:
																														_lowerCAmelCase    		=					"""running_var"""
																								elif "num_batches_tracked" in name:
																														_lowerCAmelCase    		=					"""num_batches_tracked"""
																								else:
																														_lowerCAmelCase    		=					None
																								set_recursively(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		continue
												if not is_used:
																		unused_weights.append(__lowerCamelCase						)

						logger.warning(f'Unused weights: {unused_weights}'						)


@torch.no_grad()
def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :str					,      __lowerCamelCase :int=None					,      __lowerCamelCase :str=None					,      ):
						if config_path is not None:
												_lowerCAmelCase    		=					EncodecConfig.from_pretrained(__lowerCamelCase						)
						else:
												_lowerCAmelCase    		=					EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												_lowerCAmelCase    		=					[8, 5, 4, 4]
												_lowerCAmelCase    		=					[2.2]
												_lowerCAmelCase    		=					64
												_lowerCAmelCase    		=					32000
												_lowerCAmelCase    		=					2048
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					False
						elif model_name == "encodec_48khz":
												_lowerCAmelCase    		=					[8, 5, 4, 2]
												_lowerCAmelCase    		=					[3.0, 6.0, 12.0, 24.0]
												_lowerCAmelCase    		=					48000
												_lowerCAmelCase    		=					2
												_lowerCAmelCase    		=					False
												_lowerCAmelCase    		=					"""time_group_norm"""
												_lowerCAmelCase    		=					True
												_lowerCAmelCase    		=					1.0
												_lowerCAmelCase    		=					0.01
						else:
												raise ValueError(f'Unknown model name: {model_name}'						)

						_lowerCAmelCase    		=					EncodecModel(__lowerCamelCase						)

						_lowerCAmelCase    		=					EncodecFeatureExtractor(
						    feature_size=config.audio_channels					,      sampling_rate=config.sampling_rate					,      chunk_length_s=config.chunk_length_s					,      overlap=config.overlap					,      )
						feature_extractor.save_pretrained(__lowerCamelCase						)

						_lowerCAmelCase    		=					torch.load(__lowerCamelCase						)
						if "best_state" in original_checkpoint:
												# we might have a training state saved, in which case discard the yaml results and just retain the weights
												_lowerCAmelCase    		=					original_checkpoint["""best_state"""]
						recursively_load_weights(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
						model.save_pretrained(__lowerCamelCase						)

						if repo_id:
												print("""Pushing to the hub..."""						)
												feature_extractor.push_to_hub(__lowerCamelCase						)
												model.push_to_hub(__lowerCamelCase						)


if __name__ == "__main__":
						_lowercase          =						argparse.ArgumentParser()
						parser.add_argument(
						    """--model""",
						    default="""encodec_24khz""",
						    type=str,
						    help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
						)
						parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
						)
						parser.add_argument(
						    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
						)

						_lowercase          =						parser.parse_args()
						convert_checkpoint(
						    args.model,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.push_to_hub,
						)







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



